Fix Typos-2

course/en/chapter4/section3_pt.ipynb

@@ -59,17 +59,6 @@
     "notebook_login()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -275,6 +264,9 @@
   "colab": {
    "name": "Sharing pretrained models (PyTorch)",
    "provenance": []
+  },
+  "language_info": {
+   "name": "python"
   }
  },
  "nbformat": 4,

examples/segment_anything.ipynb

@@ -325,7 +325,7 @@
         "- Generate segmentation masks per given localization (one prediction per 2D point)\n",
         "- Generate segmentation masks given a bounding box\n",
         "- Generate segmentation masks given a bounding box and a 2D points\n",
-        "- Generate segmentat"
+        "- Generate multiple segmentatation masks per image"
       ]
     },
     {

examples/text_classification.ipynb

@@ -1371,7 +1371,7 @@
    ],
    "source": [
     "trainer = Trainer(\n",
-    "    model_init=model_init,\n",
+    "    model_init=model_init(),\n",
     "    args=args,\n",
     "    train_dataset=encoded_dataset[\"train\"],\n",
     "    eval_dataset=encoded_dataset[validation_key],\n",

sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb

@@ -36,7 +36,7 @@
    "source": [
     "# Introduction\n",
     "\n",
-    "Welcome to our end-to-end `distributed` Question-Answering example. In this demo, we will use the Hugging Face `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for question-answering on multiple-gpus. In particular, the pre-trained model will be fine-tuned using the `squad` dataset. The demo will use the new `smdistributed` library to run training on multiple gpus as training scripting we are going to use one of the `transformers` [example scripts from the repository](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_qa.py).\n",
+    "Welcome to our end-to-end `distributed` Question-Answering example. In this demo, we will use the Hugging Face `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for question-answering on multiple-gpus. In particular, the pre-trained model will be fine-tuned using the `squad` dataset. The demo will use the new `smdistributed` library to run training on multiple gpus as training scripting we are going to use one of the `transformers` [example scripts from the repository](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering).\n",
     "\n",
     "To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
     "\n",

transformers_doc/en/pytorch/multiple_choice.ipynb

@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "# Transformers installation\n",
-    "! pip install transformers datasets\n",
+    "! pip install transformers datasets evaluate\n",
     "# To install from source instead of the last release, comment the command above and uncomment the following one.\n",
     "# ! pip install git+https://github.com/huggingface/transformers.git"
    ]
@@ -517,7 +517,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }